Randomforest parameter tuning R(caret) and Python(scikit learn)?

Question: is it possible, or even necessary, to perform a cross-validation check to tune the parameters of a Python randomforest implementation (eg scikit learn) when training a new model, as can be done in R’s caret?

Background R: When using R’s caret’s randomforest library, one can tune the parameters by performing a n-fold cross validation, e.g.

train_control <- trainControl(method = "repeatedcv", 
                          number = 10,
                          repeats = 5,
                          verboseIter = TRUE,
                          allowParallel = TRUE,
                          summaryFunction = multiClassSummary)
rf1 <- train(Class ~ .,
         data = train_transformed,
         method = "rf",
         metric = "Accuracy",
         tuneGrid = my_grid1,
         trControl = train_control)

This outputs –

## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 1 on full training set

or whatever mtry is the best for a dataset.

Background Python: Using scikit learn, one can instantiate a randomforest regressor and can perform a cross-validation check on that regressor

RFRegModel = RandomForestRegressor(random_state=42)
cv = cross_validate(RFRegModel,X,y,cv=5,verbose=1)
print(cv['test_score'])

the difference here is that the cross validation doesn’t appear to influence the tuning of the randomforest parameters, as I think it does in R. I think all that is happening here is that we are taking random subsamples of the training data set and testing the implementation of the

Question again: is it possible to train the model in python in a similar manner as is done in caret, i.e. by forcing an n-folds cross-validation
parameter tuning? or, is this even necessary? Is the caret implementation overconstrained by it’s methodolgy?