diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 6288ebe77c8c0..b90833bda82b5 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -7247,12 +7247,15 @@ def interpolate(
               given length of interval.
             * 'index', 'values': use the actual numerical values of the index.
             * 'pad': Fill in NaNs using existing values.
-            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'spline',
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
               'barycentric', 'polynomial': Passed to
-              `scipy.interpolate.interp1d`. These methods use the numerical
+              `scipy.interpolate.interp1d`, whereas 'spline' is passed to
+              `scipy.interpolate.UnivariateSpline`. These methods use the numerical
               values of the index.  Both 'polynomial' and 'spline' require that
               you also specify an `order` (int), e.g.
-              ``df.interpolate(method='polynomial', order=5)``.
+              ``df.interpolate(method='polynomial', order=5)``. Note that,
+              `slinear` method in Pandas refers to the Scipy first order `spline`
+              instead of Pandas first order `spline`.
             * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
               'cubicspline': Wrappers around the SciPy interpolation methods of
               similar names. See `Notes`.