L2 Regularization is NOT Just a Regularization Technique

L2 regularization serves a dual purpose beyond preventing overfitting - it also solves multicollinearity problems when features are highly correlated. The technique eliminates the valley in the residual sum of squares plot, creating a single global minimum instead of multiple parameter combinations that minimize RSS. This is why the algorithm is called ridge regression, as the L2 penalty removes the ridge in the likelihood function, enabling unique parameter estimation.