A regularization is a form of regression, which constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, it discourages learning a more complex or flexible model to avoid the risk of overfitting. It reduces the variance of the model, without a substantial increase in its bias.
Regularization is used to address overfitting problems as it penalizes the loss function by adding a multiple of an L1 (LASSO) or an L2 (Ridge) norm of weights vector w.
