# What is the use of fit method in sklearn.preprocessing.Normalizer()?

According to the documentation of fit(self, X[, y]) method of sklearn.preprocessing.Normalizer(), it does nothing and return the estimator unchanged. I understand that if I intend to normalize data I can simply pass the data to the Normalizer() function. so what is the use of use of fit method. Moreover, normalization is not a learning algorithm so why is there a fit() method?

In scikit-learn, many preprocessing operations follow the Estimator API (i.e. having fit and transform methods). The benefit of conforming to the Estimator API is that the object can be included in a data transformation pipeline. Some of the benefits of pipelines are described in the docs:
Because the Normalizer estimator is stateless, its fit method is a no-op. But if it was missing the fit method, then it could not be used in scikit-learn Pipelines.