Whether you need to train your model on your whole data set or not depends on what you want to achieve.
In the blog that you have linked, their intention was to get an estimate of what they call the “skill” of any given model that is trained on the dataset. This is useful when you are considering using multiple different kinds of models on your dataset and have to pick the best one. This is found by partitioning the data. Train on one part and validate on the other. Once they have the estimate, the model is chosen. They refer to this choosing as model finalization. Then, they leverage the entire dataset and retrain the model.