One of the most widely used standard procedures for model evaluation in classification and regression is \(K\)-fold cross-validation (CV). However, when it comes to time series forecasting, because of the inherent serial correlation and potential non-stationarity of the data, its application is not straightforward and often omitted by practitioners in favor of an out-of-sample (OOS) evaluation. In this paper, we show that the particular setup in which time series forecasting is usually performed using Machine Learning methods renders the use of standard \(K\)-fold CV possible. We present theoretical insights supporting our arguments. Furthermore, we present a simulation study where we show empirically that \(K\)-fold CV performs favorably compared to both OOS evaluation and other time-series-specific techniques such as non-dependent cross-validation.