diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 5db76fba3a937e6b26cfcd0943185c36e6f87989..dde98a01cc770c714992b88e442aef372eba7a2e 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -109,7 +109,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.errors.PerformanceWarning SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
-        -i "pandas.infer_freq SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
         -i "pandas.plotting.andrews_curves RT03,SA01" \
         -i "pandas.plotting.scatter_matrix PR07,SA01" \
diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py
index 534bee5fede44c4d6d712b8b6ddc5c6f58739f2b..9a01568971af8c9b7ec33b439dfd7c6e38f88f85 100644
--- a/pandas/tseries/frequencies.py
+++ b/pandas/tseries/frequencies.py
@@ -89,6 +89,11 @@ def infer_freq(
     """
     Infer the most likely frequency given the input index.
 
+    This method attempts to deduce the most probable frequency (e.g., 'D' for daily,
+    'H' for hourly) from a sequence of datetime-like objects. It is particularly useful
+    when the frequency of a time series is not explicitly set or known but can be
+    inferred from its values.
+
     Parameters
     ----------
     index : DatetimeIndex, TimedeltaIndex, Series or array-like
@@ -106,6 +111,13 @@ def infer_freq(
     ValueError
         If there are fewer than three values.
 
+    See Also
+    --------
+    date_range : Return a fixed frequency DatetimeIndex.
+    timedelta_range : Return a fixed frequency TimedeltaIndex with day as the default.
+    period_range : Return a fixed frequency PeriodIndex.
+    DatetimeIndex.freq : Return the frequency object if it is set, otherwise None.
+
     Examples
     --------
     >>> idx = pd.date_range(start="2020/12/01", end="2020/12/30", periods=30)