You can use pandas dataframes or series as Julien said but if you want to restrict your-self to numpy you can pass an additional array of indices:
from sklearn.model_selection import train_test_split
import numpy as np
n_samples, n_features, n_classes = 10, 2, 2
data = np.random.randn(n_samples, n_features) # 10 training examples
labels = np.random.randint(n_classes, size=n_samples) # 10 labels
indices = np.arange(n_samples)
x1, x2, y1, y2, idx1, idx2 = train_test_split(
data, labels, indices, test_size=0.2)