diff --git a/R/vfold.R b/R/vfold.R
index 66509ee3..6b3f7a0f 100644
--- a/R/vfold.R
+++ b/R/vfold.R
@@ -151,13 +151,14 @@ vfold_splits <- function(data, v = 10, strata = NULL, breaks = 4, pool = 0.1) {
 #'  on some grouping variable (which may have more than a single row
 #'  associated with it). The function can create as many splits as
 #'  there are unique values of the grouping variable or it can
-#'  create a smaller set of splits where more than one value is left
+#'  create a smaller set of splits where more than one group is left
 #'  out at a time. A common use of this kind of resampling is when you have
 #'  repeated measures of the same subject.
 #'
 #' @inheritParams vfold_cv
-#' @param v The number of partitions of the data set. If left as `NULL`, `v`
-#'  will be set to the number of unique values in the group.
+#' @param v The number of partitions of the data set. If left as `NULL` (the
+#'   default), `v` will be set to the number of unique values in the grouping
+#'   variable, creating "leave-one-group-out" splits.
 #' @param balance If `v` is less than the number of unique groups, how should
 #'  groups be combined into folds? Should be one of
 #'  `"groups"` or `"observations"`.
@@ -181,6 +182,9 @@ vfold_splits <- function(data, v = 10, strata = NULL, breaks = 4, pool = 0.1) {
 #' )
 #' group_vfold_cv(ames, group = Neighborhood, v = 5, repeats = 2)
 #'
+#' # Leave-one-group-out CV
+#' group_vfold_cv(ames, group = Neighborhood)
+#'
 #' @export
 group_vfold_cv <- function(data, group = NULL, v = NULL, repeats = 1, balance = c("groups", "observations"), ...) {
 
diff --git a/man/group_vfold_cv.Rd b/man/group_vfold_cv.Rd
index e9fecd31..a873b847 100644
--- a/man/group_vfold_cv.Rd
+++ b/man/group_vfold_cv.Rd
@@ -20,8 +20,9 @@ group_vfold_cv(
 grouping observations with the same value to either the analysis or
 assessment set within a fold.}
 
-\item{v}{The number of partitions of the data set. If left as \code{NULL}, \code{v}
-will be set to the number of unique values in the group.}
+\item{v}{The number of partitions of the data set. If left as \code{NULL} (the
+default), \code{v} will be set to the number of unique values in the grouping
+variable, creating "leave-one-group-out" splits.}
 
 \item{repeats}{The number of times to repeat the V-fold partitioning.}
 
@@ -42,7 +43,7 @@ Group V-fold cross-validation creates splits of the data based
 on some grouping variable (which may have more than a single row
 associated with it). The function can create as many splits as
 there are unique values of the grouping variable or it can
-create a smaller set of splits where more than one value is left
+create a smaller set of splits where more than one group is left
 out at a time. A common use of this kind of resampling is when you have
 repeated measures of the same subject.
 }
@@ -59,5 +60,8 @@ group_vfold_cv(
   balance = "observations"
 )
 group_vfold_cv(ames, group = Neighborhood, v = 5, repeats = 2)
+
+# Leave-one-group-out CV
+group_vfold_cv(ames, group = Neighborhood)
 \dontshow{\}) # examplesIf}
 }