GridSearch without CV
GridSearchCV is built around cross validation, but if speed is your main concern, you may be able to get better performance using a smaller number of folds.
From the docs:

class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, n_jobs=None, iid='deprecated', refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)
cv: int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy. Possible inputs for cv are:
    None, to use the default 5-fold cross validation,

    integer, to specify the number of folds in a (Stratified)KFold,

    CV splitter,

    An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

cv defaults to 5, so changing it to 2 should provide a significant speedup for you. This will weaken the cross validation significantly.
Alternatively, you may be able to pass in a single test/train split for your value of cv. This would effectively disable cross validation and remove the benefits that it provides.