The train/test split involves dividing the dataset into separate, mutually exclusive training and testing sets. Then you practise using the practise set and perform the test with the test set. As the testing dataset is separate from the dataset that was used to train the data, this will give a more accurate assessment of out-of-sample accuracy. For issues in the actual world, it is more realistic.