It means cross validation. It essentially means is a way of simulating how well your model will do when it encounters real world data.
When building a model, you divide your data into two parts, the training set and the testing set. The training set is usually larger (~80% of your original data set, although this can vary), and is used to fit your model. Then, you use the remaining data you set aside for the testing set by using your model to generate predictions for that data, and comparing it to the actual values for that data.
You can then compare the accuracy of the model for the training and testing sets to get an idea if your model generalizes well to the real world. If, for example, you find that your model has an accuracy of 95% on the training data, but 60% on your testing data, that means your model is overly tuned into features of the data used to build the model that may not actually be helpful for prediction in the real world.
I assumed Code Versioning so that if you have robust data segmentation you have less uncertainty about the impact of change. However, I'm a tourist here and hope OP comes back to share.
