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K-fold cross validation is the practice by which we separate a large data set into smaller pieces, independently process each data set, and then train our models on some number of the segments, and validate it on the rest. This is generally considered a best practice, or at least good practice,  in machine learning, as it helps ensure the correct characterization of your model on the validation set.

Machine Learning Mastery has a great post on the topic.