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This revives #235. Now that all the other elements are in place, I think that this is ready for review and in principle ready to be merged upstream (once it's been reviewed and fixed up, of course!). I am excited about this PR - evaluation of DWI models is something that I have been thinking a lot about in the past couple of years, since I started working on DWI. 

This PR also demonstrates the power and elegance of the uniform `Model`/`Fit` API we have in place. GGT!

For now, this PR only implements cross-validation for CSD and for the tensor model, but future work could extend this to other models, by implementing their `predict` methods. 

Also : a lot of PEP8 fixes in the CSD module and elsewhere :-) 
They can come from the TensorFit. In fact, the tensor fit has a `predict` method, which simply calls this function (see below): 

    return tensor_prediction(self.model_params, gtab)

The reason I implemented this as a module-wide function is because Bago suggested that these different syntax options would be nice to have: 

    FitObject.predict(gtab) == ModelObject.predict(FitObject.model_params)

And I agree fully.