For instance, many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order.
This is from the scikit-learn
Can someone please specify what elements they are referring when it is said the mean is expected to be zero.
I understand that the variance should be in similar range for the algorithms to give same significance to each feature. But is there anything also necessarily expecting zero mean in the data?
In other words, if I know variance of each feature is already in same range, can something still go wrong as it says some "elements" expect zero mean.