Fortunately, scikit-learn makes this really easy to do, and it's even easier than doing normal train/test! It's extremely simple to do k-fold cross-validation, so you may as well just do it.
Now, the way this all works in practice is you will have a model that you're trying to tune, and you will have different variations of that model, different parameters you might want to tweak on it, right?
Like, for example, the degree of polynomial for a polynomial fit. So, the idea is to try different values of your model, different variations, measure them all using k-fold cross-validation, and find the one that minimizes error against your test dataset. That's kind of your sweet spot there. In ...
