Every ML algorithm (let's start calling it estimator from now on) needs some hyperparameters to be set before it can be trained. These hyperparameters have traditionally been set by hand. Some examples of hyperparameters are step size, number of steps (learning rate), regularization parameters, and so on. 

Typically, hyperparameter tuning is a detour in model selection as you already need to know the best value of hyperparameters for training the model. At the same time, to find the right hyperparameters, you need to be able to look ahead at the accuracy. This is where evaluators come into the picture. 

In this recipe, we are going to consider an example of linear regression. The focus here is on hyperparameter tuning, so details about linear regression are skipped and covered in depth in the next chapter.