The correct option is ONNX.

ONNX is an open interchange format that represents trained machine learning models in a framework agnostic way. It defines a common model representation and operator sets so models can be exported from one framework and imported into another for inference and optimization.

The ONNX ecosystem includes runtimes and tools such as ONNX Runtime that perform cross platform inference and optimizations for models saved in the ONNX format. That broad runtime and tool support is what makes ONNX an interchange standard for trained models.

scikit-learn is a Python library for building and training classical machine learning models and not an interchange specification for saving models across frameworks.

PyTorch is a deep learning framework and not a framework agnostic model interchange standard. PyTorch can export models to ONNX but the framework itself is not the open interchange format.

TensorFlow SavedModel is TensorFlow's native serialized format for saving models and it is specific to TensorFlow. It is not an open interchange standard that is framework agnostic in the same way that ONNX is.

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