> for me the semantic is normalize = make columns unit norm 

But this is not the case here either: here it centers by column and scale by the column standard deviation to make each non-zero column scaled to unit variance.

Feature-wise centering + unit variance scaling is often referred to as "standardization": http://en.wikipedia.org/wiki/Standardizing .

That being said I am ok with leaving the `normalize` keyword of linear regression models as it is. For the non-linear ridge classifier though, I don't know what to expect. Should the non-linear ridge model do the "normalization" in the kernel space instead?