I want to prevent my model from overfitting. I think that k-fold cross-validation (because it is doing this each time with different datasets) may be more effective than splitting the dataset into training and test datasets to prevent overfitting, but a colleague (who has little experience in ML) says that, to prevent overfitting, the 70/30% split performs better than the k-fold cross-validation. In my opinion, k-fold cross-validation provides a reliable method to test the model performance.
Is k-fold cross-validation more effective than splitting the dataset into training and test datasets to prevent overfitting? I am not concerned with computational resources.