Check Accuracy of Model Provided by Consultant
Cross validation can be used in parameter tuning or model selection, but it does not evaluate the performance of a model.

When developing a model, you divide your data between train, validation and testing. In the best case scenario, testing is only used once at the end to score the model. You should definitely keep the 2016 data. 

If you give all your data, it is easy to have a model learning "by heart" your expected data but it will not generalize well to future years. This is overfiting. The only way to know is by testing it on unknown data, here, 2016

Employed for measuring model performance, cross validation can measure more than just the average accuracy and you can select your features to answer the best accuracy score