Why does the Random Forest algorithm not require split sampling methods?

Random Forest does not require a split sampling method to assess the accuracy of the model.

This is because it performs internal testing on 2/3rd of the available training data that is used to grow each tree and the remaining one-third portion of training data is always used to calculate out-of-bag error to compute the model performance.