Ridge and Lasso Regularization
The penalty of both Lasso and Ridge is proportional to the magnitude of the weight. That is, the penalization added to the cost function is $\lambda ||\omega||_2$ or $\lambda ||\omega||_1$.

Wether it is more convenient to apply regularization or feature selection, Lasso already does some feature selection for you, as the estimated weights for Lasso are sparse (there will be many coefficients equal to 0).

About multi-colinearity, Ridge tends to eliminate variables that are colinear, while Lasso doesn't.

All that I have said is taken from An Introduction to Statistical Learning book, from James, Witten, Hastie and Tibshirani.