If this is about splitting your data into training and testing data, then 80/20 is a common rule of thumb. An "optimal" split (which would need to be operationalized) would likely depend on your sample size, distributions and relationships between your variables.
It is also common to split your data three ways (e.g., 60/20/20 - again rules of thumb), into a training set that you train your models on and a test set which you test your model on. You will iterate training and testing until you like the result. Then, and only then you apply the final model (trained on both the training and test set) on the third validation set. This avoids "overfitting on the test set".
However, cross-validation is much better than a simple data split. Your textbook should also cover cross-validation. If it doesn't, get a better textbook.