Data mining algorithms such as Artificial Neural Networks (ANN) and k-Nearest Neighbour (kNN) have proven their merits in pedotransfer function modelling. Kriging is a well-known algorithm for spatial interpolation, but in this study it is proposed as an alternative data mining technique. It was compared to kNN as a benchmark pedotransfer function to predict soil water retention for a wide range of datasets, containing soil data from both temperate and tropical regions. The performance of both methods was compared through Monte Carlo cross-validation and the precision of the predictions was assessed with an ensemble procedure. Across all datasets, a significant improvement in prediction bias, accuracy and precision was found with Kriging, compared to kNN. Moreover, it was demonstrated how predictions with Kriging are more robust and insensitive to non-correlated predictor variables, and how the optimized hyperparameters provide additional insight in the training dataset properties. Kriging was found to be a accurate, precise and robust data mining solution for pedotransfer function modelling