Why do machine learning engineers insist on training with more data than validation set?
The reasoning will be: &quot;The more data for training the better&quot;. Then you have to keep in mind that the validation/hold-out set has to resemble how it should work on production/testing.  The theory is that the larger the training data, the better the model should generalize.
The validation set can be much smaller, on extremely big dataset you can make it even 0.01% of the data, and there should be no problem.
In basic cases you don't even need to do the K-fold, this makes the training more expensive and only for hyperparameter search and inside the training set it needs to be done.
For your case, you can consider the split you want. Just keep the balance of the training data to be as larger as possible and validation data to resemble the best the production environment as possible.