Cross validation is a method used in machine learning to evaluate the performance of a model on unseen data. It involves dividing the available data into multiple subsets, training the model on one subset and testing it on the remaining subsets. This process is repeated multiple times, with each subset being used as the testing set at least once. The final model performance is then determined by averaging the results of all iterations.

One example of cross validation is k-fold cross validation. In this method, the data is divided into k equal subsets, and the model is trained on k-1 subsets and tested on the remaining subset. This process is repeated k times, with each subset serving as the testing set once. The final model performance is determined by averaging the results of all iterations.

Another example is stratified k-fold cross validation. This method is similar to k-fold cross validation, but it ensures that the proportion of different classes in the data is preserved in each subset. This is particularly useful when dealing with imbalanced datasets, where one class is significantly more represented than the others.

Cross validation is an important step in the machine learning process, as it allows us to evaluate the performance of a model on unseen data and avoid overfitting. Overfitting is a common problem in machine learning, where a model performs well on the training data but poorly on new data. By using cross validation, we can ensure that our model is able to generalize to new data and not just memorize the training data.

Additionally, cross validation can help us choose the best model for our data by comparing the performance of different models on the same dataset. This can be useful when we are trying to decide between different algorithms or hyperparameters for our model.

In summary, cross validation is a powerful technique that helps us evaluate the performance of a model on unseen data and avoid overfitting. It is an essential step in the machine learning process and can be used to compare the performance of different models.