Hyperparameters Tuning
According to Wikipedia:
Hyperparameter optimization
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.
Grid Search
A set of possible hyperparameters is choosen by the trainer, and the model is trained on each element from the set. The model that gives the best performance is the model that got the best hyperparameter.
To have less biased results, the Grid Search method can be used alongside cross-validation.