diff --git a/R/PipeOpImputeHist.R b/R/PipeOpImputeHist.R
index 3ff8831b5..075ba0cfd 100644
--- a/R/PipeOpImputeHist.R
+++ b/R/PipeOpImputeHist.R
@@ -7,10 +7,11 @@
 #' @description
 #' Impute numerical features by histogram.
 #'
-#' During training, a histogram is fitted using R's [`hist()`][graphics::hist] function.
-#' The fitted histogram is then sampled from for imputation. This is an approximation to
-#' sampling from the empirical training data distribution (i.e. sampling from training data
-#' with replacement), but is much more memory efficient for large datasets, since the `$state`
+#' During training, a histogram is fitted on each column using R's [`hist()`][graphics::hist] function.
+#' The fitted histogram is then sampled from for imputation. Sampling happens in a two-step process:
+#' First, a bin is sampled from the histogram, then a value is sampled uniformly from the bin.
+#' This is an approximation to sampling from the empirical training data distribution (i.e. sampling
+#' from training data with replacement), but is much more memory efficient for large datasets, since the `$state`
 #' does not need to save the training data.
 #'
 #' @section Construction:
@@ -26,7 +27,7 @@
 #' @section Input and Output Channels:
 #' Input and output channels are inherited from [`PipeOpImpute`].
 #'
-#' The output is the input [`Task`][mlr3::Task] with all affected numeric features missing values imputed by (column-wise) histogram.
+#' The output is the input [`Task`][mlr3::Task] with all affected numeric features missing values imputed by (column-wise) histogram; see Description for details.
 #'
 #' @section State:
 #' The `$state` is a named `list` with the `$state` elements inherited from [`PipeOpImpute`].
diff --git a/man/mlr_pipeops_imputehist.Rd b/man/mlr_pipeops_imputehist.Rd
index c5abb9d72..d71500f0b 100644
--- a/man/mlr_pipeops_imputehist.Rd
+++ b/man/mlr_pipeops_imputehist.Rd
@@ -10,10 +10,11 @@
 \description{
 Impute numerical features by histogram.
 
-During training, a histogram is fitted using R's \code{\link[graphics:hist]{hist()}} function.
-The fitted histogram is then sampled from for imputation. This is an approximation to
-sampling from the empirical training data distribution (i.e. sampling from training data
-with replacement), but is much more memory efficient for large datasets, since the \verb{$state}
+During training, a histogram is fitted on each column using R's \code{\link[graphics:hist]{hist()}} function.
+The fitted histogram is then sampled from for imputation. Sampling happens in a two-step process:
+First, a bin is sampled from the histogram, then a value is sampled uniformly from the bin.
+This is an approximation to sampling from the empirical training data distribution (i.e. sampling
+from training data with replacement), but is much more memory efficient for large datasets, since the \verb{$state}
 does not need to save the training data.
 }
 \section{Construction}{
@@ -33,7 +34,7 @@ List of hyperparameter settings, overwriting the hyperparameter settings that wo
 
 Input and output channels are inherited from \code{\link{PipeOpImpute}}.
 
-The output is the input \code{\link[mlr3:Task]{Task}} with all affected numeric features missing values imputed by (column-wise) histogram.
+The output is the input \code{\link[mlr3:Task]{Task}} with all affected numeric features missing values imputed by (column-wise) histogram; see Description for details.
 }
 
 \section{State}{