We established that our models can generalize to predict solvation energy in solvents outside of the training set and for molecules larger than those used to train our datasets. Finally, we created a domain-of-applicability metric to provide users an estimate of whether the predictions of our models are trustworthy for a certain molecule. The data, models, and code used in this study are all freely available online, and the machine learning models are accessible through a simple web interface that both invokes the model and provides estimates of model uncertainty for each prediction.