Regularization is a technique used in machine learning and statistical modeling to prevent overfitting and improve the generalization performance of a model. It involves introducing a penalty term to the objective function that the model aims to minimize. This penalty term discourages the model from learning overly complex patterns that may not generalize well to unseen data. There are various types of regularization techniques, such as L1 regularization (lasso), L2 regularization (ridge), and elastic net regularization, each of which imposes different constraints on the model parameters. Regularization helps to strike a balance between bias and variance in the model by penalizing overly complex models without sacrificing too much on the model's performance on training data.