Regularization is a technique that's used to reduce model overfitting. There are two main approaches to regularization. The first one is known as training data preprocessing. The second one is loss function modification. The main idea of the loss function modification techniques is to add terms to the loss function that penalize algorithm results, thereby leading to significant variance. The idea of training data preprocessing techniques is to add more distinct training samples. Usually, in such an approach, new training samples are generated by augmenting existing ones. In general, both approaches add some prior knowledge about the task domain to the model. This additional information helps us with variance regularization. Therefore, we can conclude that regularization is any technique that leads to minimizing the generalization error.