It is necessary to have a set of proper hyperparameter values to create a good ML model. The reason for this is that having random values leads to controversial results and behaviors that are not expected by the practitioner. There are several approaches we can follow to choose the best set of hyperparameter values. We can try to use hyperparameters from the algorithms we have already trained that are similar to our task. We can also try to find some heuristics and tune them manually. However, this task can be automated. The grid search technique is the automated approach for searching for the best hyperparameter values. It uses the cross-validation technique for model performance estimation.