I am reading Hands-on Machine Learning with Scikit-Learn & TensorFlow by Aurelien Geron. In chapter 4: Training models page 122, where it is explaining linear regression using SGD, it says that
Note that since instances are picked randomly, some instances may be picked several times per epoch while others may not be picked at all. If you want to be sure that the algorithm goes through every instance at each epoch, another approach is to shuffle the training set, then go through it instance by instance, then shuffle it again, and so on. However, this generally converges more slowly.
I am not sure how the last sentence can be justified. How using some of the same instances and not using some can be better than going through instance by instance using all instances one time?