Regularization to overcome the problem of overfitting.
Regularization refers to a broad range of techniques for artificially forcing your model to be simpler.
The method will depend on the type of learner you’re using. For example, you could prune a decision tree, use dropout on a neural network, or add a penalty parameter to the cost function in regression.
Oftentimes, the regularization method is a hyperparameter as well, which means it can be tuned through cross-validation.