The comparable performance exhibited by different ML models with distinct architectures in predicting bulk and shear modulus suggests a limited learnability from the dataset and feature representation. While many features in the feature space are immutable, particularly the properties of elemental systems, the mutable ones are the geometric attributes or structural representations. For the prediction task of the machine learning models discussed so far, the SOAP representation of structural attributes is based on predefined mathematical formulations. Thus, we conclude that the lack of flexibility in the structural representation is the primary reason for the saturation in predictability. In this context, the structure itself, providing more flexibility in feature representation but requiring a larger amount of data.