I am sorry, I have a simple question that I am confused about (I AM STILL A BEGINNER): 

When I create a model let's say a decision tree model and I specify random_state=integer to get reproducible outputs, then I run cross validation (let's say kfold with k=5) and I also specify random_state=integer in my CV to get reproducible outputs, then take the average R^2 for my kfolds, is this enough to give me a clue about how good is my model? 

    new_model = DecisionTreeRegressor(max_depth=9,
                                      min_samples_split=2,random_state=0)
    
    crossvalidation_Decision_Trees = KFold(n_splits=5, random_state=0,shuffle=True)  
    model2=new_model.fit(X_normalized, y_for_normalized)
        
    scores_D_Trees = cross_val_score(model2, X_normalized,y_for_normalized, scoring='r2', cv=crossvalidation_Decision_Trees,
     n_jobs=1)
        
    print("\n\nDecision Trees"+": R^2 for every fold: " + str(scores_D_Trees))
    
    print('\033[1m'+"Decision Trees"+'\033[1m'+": Average R^2 for all the folds: " + str(np.mean(scores_D_Trees)) + '\033[0m'+ ", STD: " + str(np.std(scores_D_Trees)))


                                           OR: 

Shall I remove the random_state from my decision tree model AND from my CV and let the code take different training and testing datasets every time I run the code, repeat that many times (let's say iterations=5) and at the end take the average R^2 for the average R^2 of my kfolds for these 5 iterations as an indicator for my model's performance? Will this be a better evaluation of my model? 



new_model = DecisionTreeRegressor(max_depth=9,
                                      min_samples_split=2)
    
    crossvalidation_Decision_Trees = KFold(n_splits=5,shuffle=True)  
    model2=new_model.fit(X_normalized, y_for_normalized)
        
    scores_D_Trees = cross_val_score(model2, X_normalized,y_for_normalized, scoring='r2', cv=crossvalidation_Decision_Trees,
     n_jobs=1)
        
    print("\n\nDecision Trees"+": R^2 for every fold: " + str(scores_D_Trees))
    
    print('\033[1m'+"Decision Trees"+'\033[1m'+": Average R^2 for all the folds: " + str(np.mean(scores_D_Trees)) + '\033[0m'+ ", STD: " + str(np.std(scores_D_Trees)))


                                           OR: 

Any of these approaches is acceptable?