Always Normalize Features

[epeters3]

Many optimization strategies do best when the features are all normalized. Rather than currently requiring normalization to be found as a preprocessor when sampling, always insert a normalization preprocessing step, with an option to not normalize (e.g. have normalize=True be a default in the crank API).

[epeters3]

I think it makes sense to still include the normalization primitives as searchable options since it may be valuable to have normalization again after some prediction or other preprocessing that again alters the range of the data in the ML pipeline.