Regularization is a strategy for dealing with the problem of model overfitting. Overfitting occurs when a complicated model is applied to training data. The basic model may not be able to generalize the data at times, and the complicated model may overfit the data. Regularization is used to alleviate this issue. Regularization is the process of adding coefficient terms (betas) to the minimization problem in such a way that the terms are penalized and have a modest magnitude. This essentially aids in identifying data patterns while also preventing overfitting by preventing the model from becoming too complex.