The primary purpose of early stopping techniques in Spark ML model training is to halt the training process when the model's performance on a validation dataset no longer improves. This method acts as a regularization technique to monitor and assess the model's effectiveness during training. By stopping early when further training is unlikely to enhance the model's generalization to new data, early stopping helps prevent overfitting, ensuring the development of a more robust and efficient model.