Regularization is a technique used to prevent overfitting in machine learning models. It can be divided into two types: explicit and implicit regularization. Explicit regularization involves adding a term to the optimization problem, such as priors, penalties, or constraints. Implicit regularization includes techniques such as early stopping, using a robust loss function, and discarding outliers. Regularization mitigates overfitting by managing the complexity of the model. ^{1 sharpsightlabs.com 2}