GridSearchCV with Random Forest Classifier
You can definitely use GridSearchCV with Random Forest. In fact you should use GridSearchCV to find the best parameters that will make your oob_score very high.
Some parameters to tune are:
n_estimators: Number of tree your random forest should have. The more n_estimators the less overfitting. You should try from 100 to 5000 range.
max_depth: max_depth of each tree. You should specify certain max_depth so that your model don't memorise train examples.
min_sample_split: the minimum number of samples to have before splitting into new nodes.
and many more...
But these are the main hyperparameters we tune to get a forest that works well.And to get these hyperparams you should use GridSeachCV.