Difference between Classification and Regression learners

[lincj1994]

Hi,
I have a question regarding the difference between Classification and Regression learners (e.g., classif.glmnet vs regr.glmnet, and classif.randomForest vs regr.randomForest). From my initial understanding, both types of learners seem to work well for prediction tasks where the goal is to build a predictive model for a two-class dependent variable.
I know that Classification learners are typically used for predicting class labels (e.g., 0 or 1, or multiple categories), while Regression learners are designed for predicting continuous outcomes. However, I believe that Regression learners can also be used for predicting class labels. Is this correct?
Could someone clarify the distinctions between these learners and when to use each one?
Thanks!