Cross-validation is a method to validate an estimated hypothesis on data. In the beginning of the analysis process, the data is split into the learning data and the testing data. A hypothesis is fit to the learning data, then its actual error is measured on the testing data. This way, we can estimate how well a hypothesis may perform on the future data. Reducing the amount of learning data can also be beneficial in the end, as it reduces the chance of hypothesis over-fitting – a hypothesis being trained to a particular narrow data subset of the data.