Is there easy way to grid search without cross validation in python?
Although the question has been solved years ago, I just found a more natural way if you insist on using GridSearchCV() instead of other means (ParameterGrid(), etc.): 

Create a sklearn.model_selection.PredefinedSplit(). It takes a parameter called test_fold, which is a list and has the same size as your input data. In the list, you set all samples belonging to training set as -1 and others as 0.
Create a GridSearchCV object with cv="the created PredefinedSplit object".

Then, GridSearchCV will generate only 1 train-validation split, which is defined in test_fold.
There is absolutely helpful class GridSearchCV in scikit-learn to do grid search and cross validation, but I don't want to do cross validataion. I want to do grid search without cross validation and use whole data to train.
To be more specific, I need to evaluate my model made by RandomForestClassifier with "oob score" during grid search.
Is there easy way to do it? or should I make a class by myself?
The points are

I'd like to do grid search with easy way.
I don't want to do cross validation.
I need to use whole data to train.(don't want to separate to train data and test data)
I need to use oob score to evaluate during grid search.