When to use cross-validation?
In a short answer, It seems that you used the same hyperparameters for both cross-validation's last training and the simple training process (without cross validation).
In the long answer

In K-fold cross-validation, you tune the hyperparameters according to the mean of K performance in order to obtain the maximum generalizing ability of the model.
Then you train the model last time with the chosen hyperparameters using the whole training set. If you have a test set you use that for the final evaluating of your model.
To sum up, the last step of the training part is training the model with the whole training set even if you use k-fold cross-validation or not.
So it seems that with cross-validation and without cross-validation you use the same hyperparameters as &quot;max_depth&quot;, &quot;minimum sample leaf etc.&quot;