What is OOF approach in machine learning?

I found this article from machine learning mastery explaining out of the fold predictions quite in depth. Below an extract from the article explaining what out of fold (OOF) prediction is:

"An out-of-fold prediction is a prediction by the model during the k-fold cross-validation procedure. That is, out-of-fold predictions are those predictions made on the holdout datasets during the resampling procedure. If performed correctly, there will be one prediction for each example in the training dataset."

OOF simply stands for "Out-of-fold" and refers to a step in the learning process when using k-fold validation in which the predictions from each set of folds are grouped together into one group of 1000 predictions. These predictions are now "out-of-the-folds" and thus error can be calculated on these to get a good measure of how good your model is.

In terms of learning more about it, there's really not a ton more to it than that, and it certainly isn't its own technique to learning or anything. If you have a follow up question that is small, please leave a comment and I will try and update my answer to include this.

EDIT: While ambling around the inter-webs I stumbled upon this relatively similar question from Cross-Validated (with a slightly more detailed answer), perhaps it will add some intuition if you are still confused.