The mean of multiple train/test splits is required in order to evaluate an ML model(?)

I can't even remember where I picked this up, but my understanding is that when building an ML model, and you want to know how well that model really predicts on unseen (test) data - to give you an indication of how well it will perform in the field, one doesn't just do a train test split once and evaluate your model on the test set, but rather, one should do this many times and take the mean performance from …

datascience mean test