that' all it is it is more robust way of doing train/testand that' one way of doing it nowyou might think to yourself wellwhat if ' overfitting to that one test dataset that reservedi' still using the same test dataset for every one of those training datasets what if that test dataset isn' really representative of things eitherthere are variations of -fold cross-validation that will randomize that as well soyou could randomly pick what the training dataset is as well each time aroundand just keep randomly assigning things to different buckets and measuring the results but usuallywhen people talk about -fold cross-validationthey're talking about this specific technique where you reserve one bucket for testingand the remaining buckets for trainingand you evaluate all of your training datasets against the test dataset when you build model for each one example of -fold cross-validation using scikitlearn fortunatelyscikit-learn makes this really easy to doand it' even easier than doing normal train/testit' extremely simple to do -fold cross-validationso you may as well just do it nowthe way this all works in practice is you will have model that you're trying to tuneand you will have different variations of that modeldifferent parameters you might want to tweak on itrightlikefor examplethe degree of polynomial for polynomial fit sothe idea is to try different values of your modeldifferent variationsmeasure them all using -fold crossvalidationand find the one that minimizes error against your test dataset that' kind of your sweet spot there in practiceyou want to use -fold cross-validation to measure the accuracy of your model against test datasetand just keep refining that modelkeep trying different values within itkeep trying different variations of that model or maybe even different models entirelyuntil you find the technique that reduces error the mostusing kfold cross validation let' go dive into an example and see how it works we're going to apply this to our iris dataset againrevisiting svcand we'll play with -fold cross-validation and see how simple it is let' actually put -fold cross-validation and train/test into practice here using some real python code you'll see it' actually very easy to usewhich is good thing because this is technique you should be using to measure the accuracythe effectiveness of your models in supervised learning