In a nutshell
A network big enough to memorize its training data will overfit — training loss keeps dropping while loss on unseen data climbs. Regularization keeps it honest. Dropout randomly switches off neurons each step so none can rely on a specific partner, forcing robust, redundant features. Weight penalties L2 (shrink large weights) and L1 (push some to zero) discourage overcomplicated fits. The goal is generalization: doing as well on new data as on the training set.