Cross validation using the fold technique provides us with a way to evaluate the performance of a machine learning model. We divide our data into ‘k’ equal portions or segments. Then we train the model on ‘k 1’ segments. Test it on the remaining segment. This process is repeated ‘k’ times with each iteration testing, on a segment.

For instance if we employ 5 fold cross validation we split the data into 5 parts. The model is trained on 4 parts. Tested on the part. We repeat this process 5 times testing each part once. Finally we average the results, from all 5 tests to assess how well the model performs overall.