Overfitting occurs when we have a small dataset, and a model is trying to learn from it. By using a large amount of data, overfitting can be avoided. But if we have a small database and are forced to build a model based on that, then we can use a technique known as cross-validation. In this method, a model is usually given a dataset of a known data on which training data set is run and dataset of unknown data against which the model is tested. The primary aim of cross-validation is to define a dataset to "test" the model in the training phase. If there is sufficient data, 'Isotonic Regression' is used to prevent overfitting.
