diff --git a/R/EnsembleFSResult.R b/R/EnsembleFSResult.R
index e37250f7..54ad93d5 100644
--- a/R/EnsembleFSResult.R
+++ b/R/EnsembleFSResult.R
@@ -17,27 +17,27 @@
 #'
 #' @examples
 #' \donttest{
-#' efsr = ensemble_fselect(
-#'   fselector = fs("rfe", n_features = 2, feature_fraction = 0.8),
-#'   task = tsk("sonar"),
-#'   learners = lrns(c("classif.rpart", "classif.featureless")),
-#'   init_resampling = rsmp("subsampling", repeats = 2),
-#'   inner_resampling = rsmp("cv", folds = 3),
-#'   measure = msr("classif.ce"),
-#'   terminator = trm("none")
-#' )
+#'   efsr = ensemble_fselect(
+#'     fselector = fs("rfe", n_features = 2, feature_fraction = 0.8),
+#'     task = tsk("sonar"),
+#'     learners = lrns(c("classif.rpart", "classif.featureless")),
+#'     init_resampling = rsmp("subsampling", repeats = 2),
+#'     inner_resampling = rsmp("cv", folds = 3),
+#'     measure = msr("classif.ce"),
+#'     terminator = trm("none")
+#'   )
 #'
-#' # contains the benchmark result
-#' efsr$benchmark_result
+#'   # contains the benchmark result
+#'   efsr$benchmark_result
 #'
-#' # contains the selected features for each iteration
-#' efsr$result
+#'   # contains the selected features for each iteration
+#'   efsr$result
 #'
-#' # returns the stability of the selected features
-#' efsr$stability(stability_measure = "jaccard")
+#'   # returns the stability of the selected features
+#'   efsr$stability(stability_measure = "jaccard")
 #'
-#' # returns a ranking of all features
-#' head(efsr$feature_ranking())
+#'   # returns a ranking of all features
+#'   head(efsr$feature_ranking())
 #' }
 EnsembleFSResult = R6Class("EnsembleFSResult",
   public = list(
@@ -149,6 +149,9 @@ EnsembleFSResult = R6Class("EnsembleFSResult",
     #'  Whether to calculate the stability globally or for each learner.
     #' @param reset_cache (`logical(1)`)\cr
     #'  If `TRUE`, the cached results are ignored.
+    #'
+    #' @return A `numeric()` value representing the stability of the selected features.
+    #' Or a `numeric()` vector with the stability of the selected features for each learner.
     stability = function(stability_measure = "jaccard", ..., global = TRUE, reset_cache = FALSE) {
       funs = stabm::listStabilityMeasures()$Name
       keys = tolower(gsub("stability", "", funs))
@@ -175,8 +178,6 @@ EnsembleFSResult = R6Class("EnsembleFSResult",
         private$.stability_learner[[stability_measure]] = set_names(tab$score, tab$learner_id)
         private$.stability_learner[[stability_measure]]
       }
-
-
     }
   ),
 
diff --git a/R/ensemble_fselect.R b/R/ensemble_fselect.R
index 87942adf..afde7803 100644
--- a/R/ensemble_fselect.R
+++ b/R/ensemble_fselect.R
@@ -5,6 +5,7 @@
 #' @description
 #' Ensemble feature selection using multiple learners.
 #' The ensemble feature selection method is designed to identify the most informative features from a given dataset by leveraging multiple machine learning models and resampling techniques.
+#' Returns an [EnsembleFSResult].
 #'
 #' @details
 #' The method begins by applying an initial resampling technique specified by the user, to create **multiple subsamples** from the original dataset.
@@ -42,7 +43,7 @@
 #' @export
 #' @examples
 #' \donttest{
-#'   ensemble_fselect(
+#'   efsr = ensemble_fselect(
 #'     fselector = fs("random_search"),
 #'     task = tsk("sonar"),
 #'     learners = lrns(c("classif.rpart", "classif.featureless")),
@@ -51,6 +52,7 @@
 #'     measure = msr("classif.ce"),
 #'     terminator = trm("evals", n_evals = 10)
 #'   )
+#'   efsr
 #' }
 ensemble_fselect = function(
   fselector,
diff --git a/man/ensemble_fs_result.Rd b/man/ensemble_fs_result.Rd
index c394dda7..d5720cfb 100644
--- a/man/ensemble_fs_result.Rd
+++ b/man/ensemble_fs_result.Rd
@@ -25,27 +25,27 @@ Whether to add the learner, task and resampling information from the benchmark r
 
 \examples{
 \donttest{
-efsr = ensemble_fselect(
-  fselector = fs("rfe", n_features = 2, feature_fraction = 0.8),
-  task = tsk("sonar"),
-  learners = lrns(c("classif.rpart", "classif.featureless")),
-  init_resampling = rsmp("subsampling", repeats = 2),
-  inner_resampling = rsmp("cv", folds = 3),
-  measure = msr("classif.ce"),
-  terminator = trm("none")
-)
+  efsr = ensemble_fselect(
+    fselector = fs("rfe", n_features = 2, feature_fraction = 0.8),
+    task = tsk("sonar"),
+    learners = lrns(c("classif.rpart", "classif.featureless")),
+    init_resampling = rsmp("subsampling", repeats = 2),
+    inner_resampling = rsmp("cv", folds = 3),
+    measure = msr("classif.ce"),
+    terminator = trm("none")
+  )
 
-# contains the benchmark result
-efsr$benchmark_result
+  # contains the benchmark result
+  efsr$benchmark_result
 
-# contains the selected features for each iteration
-efsr$result
+  # contains the selected features for each iteration
+  efsr$result
 
-# returns the stability of the selected features
-efsr$stability(stability_measure = "jaccard")
+  # returns the stability of the selected features
+  efsr$stability(stability_measure = "jaccard")
 
-# returns a ranking of all features
-head(efsr$feature_ranking())
+  # returns a ranking of all features
+  head(efsr$feature_ranking())
 }
 }
 \section{Public fields}{
@@ -168,19 +168,13 @@ The method to calculate the feature ranking.}
 \if{html}{\out{</div>}}
 }
 \subsection{Details}{
-The feature ranking process is built on the following framework: models
-act as voters, features act as candidates, and voters select certain
-candidates (features). The primary objective is to compile these selections
-into a consensus ranked list of features, effectively forming a committee.
-Currently, only \code{"approval_voting"} method is supported, which selects the
-candidates/features that have the highest approval score or selection
-frequency, i.e. appear the most often.
+The feature ranking process is built on the following framework: models act as voters, features act as candidates, and voters select certain candidates (features).
+The primary objective is to compile these selections into a consensus ranked list of features, effectively forming a committee.
+Currently, only \code{"approval_voting"} method is supported, which selects the candidates/features that have the highest approval score or selection frequency, i.e. appear the most often.
 }
 
 \subsection{Returns}{
-A \link[data.table:data.table]{data.table} listing all the features,
-ordered by decreasing inclusion probability scores (depending on the
-\code{method})
+A \link[data.table:data.table]{data.table::data.table} listing all the features, ordered by decreasing inclusion probability scores (depending on the \code{method})
 }
 }
 \if{html}{\out{<hr>}}
@@ -194,6 +188,7 @@ When the same stability measure is requested again with different arguments, the
 \if{html}{\out{<div class="r">}}\preformatted{EnsembleFSResult$stability(
   stability_measure = "jaccard",
   ...,
+  global = TRUE,
   reset_cache = FALSE
 )}\if{html}{\out{</div>}}
 }
@@ -209,11 +204,18 @@ Default is \code{"jaccard"}.}
 \item{\code{...}}{(\code{any})\cr
 Additional arguments passed to the stability measure function.}
 
+\item{\code{global}}{(\code{logical(1)})\cr
+Whether to calculate the stability globally or for each learner.}
+
 \item{\code{reset_cache}}{(\code{logical(1)})\cr
 If \code{TRUE}, the cached results are ignored.}
 }
 \if{html}{\out{</div>}}
 }
+\subsection{Returns}{
+A \code{numeric()} value representing the stability of the selected features.
+Or a \code{numeric()} vector with the stability of the selected features for each learner.
+}
 }
 \if{html}{\out{<hr>}}
 \if{html}{\out{<a id="method-EnsembleFSResult-clone"></a>}}
diff --git a/man/ensemble_fselect.Rd b/man/ensemble_fselect.Rd
index 72dd0ff7..6a8454f1 100644
--- a/man/ensemble_fselect.Rd
+++ b/man/ensemble_fselect.Rd
@@ -73,6 +73,7 @@ an \link{EnsembleFSResult} object.
 \description{
 Ensemble feature selection using multiple learners.
 The ensemble feature selection method is designed to identify the most informative features from a given dataset by leveraging multiple machine learning models and resampling techniques.
+Returns an \link{EnsembleFSResult}.
 }
 \details{
 The method begins by applying an initial resampling technique specified by the user, to create \strong{multiple subsamples} from the original dataset.
@@ -84,7 +85,7 @@ Results are stored in an \link{EnsembleFSResult}.
 }
 \examples{
 \donttest{
-  ensemble_fselect(
+  efsr = ensemble_fselect(
     fselector = fs("random_search"),
     task = tsk("sonar"),
     learners = lrns(c("classif.rpart", "classif.featureless")),
@@ -93,5 +94,6 @@ Results are stored in an \link{EnsembleFSResult}.
     measure = msr("classif.ce"),
     terminator = trm("evals", n_evals = 10)
   )
+  efsr
 }
 }