A data scientist is building a Spark ML pipeline to train a LogisticRegression model on a DataFrame that contains several numeric columns: 'age', 'income', and 'tenure', plus a 'label' column. When they add the LogisticRegression estimator directly as a pipeline stage and call fit(), the job fails because the model cannot find its expected input column. What stage must be added to the pipeline before the estimator to resolve this?