What is Ground Truth
The ground truth is what you measured for your target variable for the training and testing examples.

Nearly all the time you can safely treat this the same as the label.

In some cases it is not precisely the same as the label. For instance if you augment your data set, there is a subtle difference between the ground truth (your actual measurements) and how the augmented examples relate to the labels you have assigned. However, this distinction is not usually a problem.

Ground truth can be wrong. It is a measurement, and there can be errors in it. In some ML scenarios it can also be a subjective measurement where it is difficult define an underlying objective truth - e.g.  expert opinion or analysis, which you are hoping to automate. Any ML model you train will be limited by the quality of the ground truth used to train and test it, and that is part of the explanation on the Wikipedia quote. It is also why published articles about ML should include full descriptions of how the data was collected.