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To train a robust machine learning model, one must select the correct machine learning algorithm with the correct combination of hyperparameters. The process of choosing the optimal set of parameters is known as hyperparameter tuning. One must train the dataset on all machine learning algorithms and on a different combination of its hyperparameters to improve the performance metric. The cross-validation technique can be used to train the dataset on various machine learning algorithms and choose the best out of it. Cross-Validation is a resampling technique that can be used to evaluate and select machine learning algorithms on a limited dataset.