diff --git a/modin/pandas/groupby.py b/modin/pandas/groupby.py
index cefa68ef8e1..70c7a8ec26e 100644
--- a/modin/pandas/groupby.py
+++ b/modin/pandas/groupby.py
@@ -137,24 +137,6 @@ def sem(self, ddof=1):
             numeric_only=True,
         )
 
-    def value_counts(
-        self,
-        subset=None,
-        normalize: bool = False,
-        sort: bool = True,
-        ascending: bool = False,
-        dropna: bool = True,
-    ):
-        return self._default_to_pandas(
-            lambda df: df.value_counts(
-                subset=subset,
-                normalize=normalize,
-                sort=sort,
-                ascending=ascending,
-                dropna=dropna,
-            )
-        )
-
     def mean(self, numeric_only=None):
         return self._check_index(
             self._wrap_aggregation(
@@ -1356,24 +1338,6 @@ def nsmallest(self, n=5, keep="first"):
                 numeric_only=True,
             )
         )
-        
-    def value_counts(
-        self,
-        normalize: bool = False,
-        sort: bool = True,
-        ascending: bool = False,
-        bins=None,
-        dropna: bool = True,
-    ):
-        return self._default_to_pandas(
-            lambda ser: ser.value_counts(
-                normalize=normalize,
-                sort=sort,
-                ascending=ascending,
-                bins=bins,
-                dropna=dropna,
-            )
-        )
 
 
 if IsExperimental.get():
diff --git a/modin/pandas/test/test_groupby.py b/modin/pandas/test/test_groupby.py
index e3dea733fb7..5819aced627 100644
--- a/modin/pandas/test/test_groupby.py
+++ b/modin/pandas/test/test_groupby.py
@@ -227,7 +227,6 @@ def test_mixed_dtypes_groupby(as_index):
         eval_sum(modin_groupby, pandas_groupby)
         eval_ngroup(modin_groupby, pandas_groupby)
         eval_nunique(modin_groupby, pandas_groupby)
-        eval_value_counts(modin_groupby, pandas_groupby)
         eval_median(modin_groupby, pandas_groupby)
         eval_general(modin_groupby, pandas_groupby, lambda df: df.head(n))
         eval_cumprod(modin_groupby, pandas_groupby)
@@ -626,7 +625,6 @@ def test_single_group_row_groupby():
     eval_sum(modin_groupby, pandas_groupby)
     eval_ngroup(modin_groupby, pandas_groupby)
     eval_nunique(modin_groupby, pandas_groupby)
-    eval_value_counts(modin_groupby, pandas_groupby)
     eval_median(modin_groupby, pandas_groupby)
     eval_general(modin_groupby, pandas_groupby, lambda df: df.head(n))
     eval_cumprod(modin_groupby, pandas_groupby)
@@ -745,7 +743,6 @@ def test_large_row_groupby(is_by_category):
     eval_sum(modin_groupby, pandas_groupby)
     eval_ngroup(modin_groupby, pandas_groupby)
     eval_nunique(modin_groupby, pandas_groupby)
-    eval_value_counts(modin_groupby, pandas_groupby)
     eval_median(modin_groupby, pandas_groupby)
     eval_general(modin_groupby, pandas_groupby, lambda df: df.head(n))
     # eval_cumprod(modin_groupby, pandas_groupby) causes overflows
@@ -853,8 +850,6 @@ def test_simple_col_groupby():
     # Pandas fails on this case with ValueError
     # eval_ngroup(modin_groupby, pandas_groupby)
     # eval_nunique(modin_groupby, pandas_groupby)
-    # NotImplementedError: DataFrameGroupBy.value_counts only handles axis=0
-    # eval_value_counts(modin_groupby, pandas_groupby)
     eval_median(modin_groupby, pandas_groupby)
     eval_general(
         modin_groupby,
@@ -981,7 +976,6 @@ def test_series_groupby(by, as_index_series_or_dataframe):
         eval_size(modin_groupby, pandas_groupby)
         eval_ngroup(modin_groupby, pandas_groupby)
         eval_nunique(modin_groupby, pandas_groupby)
-        eval_value_counts(modin_groupby, pandas_groupby)
         eval_median(modin_groupby, pandas_groupby)
         eval_general(modin_groupby, pandas_groupby, lambda df: df.head(n))
         eval_cumprod(modin_groupby, pandas_groupby)
@@ -1138,10 +1132,7 @@ def eval_nunique(modin_groupby, pandas_groupby):
     df_equals(modin_groupby.nunique(), pandas_groupby.nunique())
 
 
-def eval_value_counts(modin_groupby, pandas_groupby):
-    df_equals(modin_groupby.value_counts(), pandas_groupby.value_counts())
-
-
+@_copy_pandas_groupby_if_needed
 def eval_median(modin_groupby, pandas_groupby):
     modin_df_almost_equals_pandas(modin_groupby.median(), pandas_groupby.median())