Scikit-learn: preprocessing.scale() vs preprocessing.StandardScalar()

I understand that scaling means centering the mean(mean=0) and making unit variance(variance=1).

But, What is the difference between preprocessing.scale(x)and preprocessing.StandardScalar() in scikit-learn?

Those are doing exactly the same, but:

• preprocessing.scale(x) is just a function, which transforms some data
• preprocessing.StandardScaler() is a class supporting the Transformer API

I would always use the latter, even if i would not need inverse_transform and co. supported by StandardScaler().

Excerpt from the docs:

The function scale provides a quick and easy way to perform this operation on a single array-like dataset

The preprocessing module further provides a utility class StandardScaler that implements the Transformer API to compute the mean and standard deviation on a training set so as to be able to later reapply the same transformation on the testing set. This class is hence suitable for use in the early steps of a sklearn.pipeline.Pipeline