What does it mean to cross-validate a machine learning model?

Cross-validation is used for testing the model on new data that the model has never seen before. The best example is when you use Scikit Learn (or any other library) to split your data into training and test set. The test set data is used to cross-validate your model after it is trained, so you can assess how well your model is performing.