Here's the guide I'm looking at: http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/
Here's the relevant excerpt: The main point to take home is that we’re using the predictions of the base models as features (i.e. meta features) for the stacked model. So, the stacked model is able to discern where each model performs well and where each model performs poorly. It’s also important to note that the meta features in row $i$ of train_meta are not dependent on the target value in row $i$ because they were produced using information that excluded the target_i in the base models’ fitting procedure.
Could somebody elaborate on why it is important that the meta features are not dependent on the corresponding label?