from sklearn.model_selection import train_test_split

As an AI language model, I am not able to perform imports or run code. However, I can explain what the `train_test_split` function does in scikit-learn.

`train_test_split` is a function provided by scikit-learn that splits a dataset into two subsets: a training set and a testing set. This function is commonly used for evaluating the performance of a machine learning model.

The function takes in several arguments, including:

- `arrays`: The dataset to be split, provided as one or more arrays or matrices.
- `test_size`: The proportion of the dataset that should be allocated to the testing set.
- `train_size`: The proportion of the dataset that should be allocated to the training set. This is an alternative to `test_size` that specifies the training set size directly.
- `random_state`: A seed value that ensures the same random split is generated each time the function is called.

The function returns four values: `X_train`, `X_test`, `y_train`, and `y_test`, which represent the training and testing sets for the input data and target values, respectively. The training and testing sets are randomly sampled from the input data, with the testing set being a subset of the input data that is not included in the training set.