Determine model hyper-parameter values for grid search
I am not sure if I understand your question correctly, but in your model, you are tuning your "alpha" parameter, you have a range from 1 to 0. (1 -> 0.1 -> 0.01 -> 0.001 -> 0.0001 -> 0).

The grid search will evaluate each algorithm (SVD, CHOLESKY,...) with each possible value of your "alpha" parameter.
It will define the score for each alpha parameter (eg. accuracy  / auc). The score metric depends on your estimator / choice

Documentation:

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html