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


  Pipeline can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification. Pipeline serves multiple purposes here:
  
  Convenience and encapsulation:
  You only have to call fit and predict once on your data to fit a whole sequence of estimators.
  
  Joint parameter selection:
  You can grid search over parameters of all estimators in the pipeline at once.
  
  Safety:
   Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors. 


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.