The goal of the model is to identify the line that fits most data points which falls within a predetermined margin (prediction error). Such models have been successfully applied to organocatalyzed reactions such as the chiral phosphoric acid catalyzed thiol-nucleophiles addition to imines. The major difference between MLR and SVR is that SVR does not have an underlying assumption of a linear relationship between the data points (descriptors) and the outcome (enantioselectivity), and therefore, can be more suitable for modeling complex reactions. On the other hand, MLR can be more informative for the interpretation of the influence of each descriptor (e.g., catalyst structure, solvent, temperature), as the coefficients also contribute to their weight of influence. An SVR model is used for predicting an outcome, and the model itself, often used as a black box, is not easily interpreted. Each model has its strengths and weaknesses, and they are used based on the main goals of the modeling project as well as the existing data. It is worth noting that other regression models such as kernel ridge and partial least squares (PLS, also referred to as projection to latent structures) have also been used. Once a predictive model has been developed and tested, it should then be adopted by organic chemists. As the field remains in its infancy, most published ML models and methods are only made available in the form of scripts and methodologies. However, there is no available user-friendly package that can be used or models that can easily be trained for novel reactions by organic chemists with minimal expertise in computer science, although this may soon change.