What does "cv" parameter mean in gridsearchcv() in sklearn ?

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Python

machine-learning

model_object = SVC()

hyperparameters = {
"C":[1,10,20],
"kernel":["rbf","linear"]
}

grid = GridSearchCV(model_object,hyperparameters,cv=5,return_train_score=False)
grid.fit(x,y)

gridSearch_result = grid.cv_results_

Above I am trying to tune some hyperparameters using sklearn GridSearchCV() , and among's other parameter I am not getting what is the purpose of the parameter "cv".

I looked online and also in the documentation and it says "number of cross validations to perform" , but what does that mean ? I know K-Fold and other cross validation methods. Does cv means the value of K ?

What I have tried:

I tried looking online into other resources