Regularization in machine learning is a technique used to prevent overfitting by adding a penalty to the loss function. It discourages the model from fitting too closely to the training data by constraining or regularizing the coefficient estimates towards zero, thus simplifying the model. Common methods include L1 (Lasso) and L2 (Ridge) regularization.