Cross-validation is a technique used to evaluate a machine learning model by splitting the data into training and testing sets multiple times. This allows the model to be trained and tested on different data each time, providing a more reliable estimate of model performance.
For example, if we have a dataset with 1000 observations, we can split it into 10 sets of 100 observations each. We can then use 9 of the sets for training and the remaining 1 for testing. We can repeat this process 10 times, using a different set for testing each time. The average performance of the model on the 10 tests can then be used to evaluate the model.